In artificial intelligence (AI), difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem, which is making computers as intelligent as people, also referred to as strong AI. An AI-complete problem is one not solved by a simple specific algorithm. AI-complete problems include computer vision, natural language understanding, dealing with unexpected circumstances while solving any real world problem, and the like. Currently, AI-complete problems cannot be solved with modern computer technology alone.
Current AI systems can solve very simple restricted versions of AI-complete problems, but never in their full generality. When AI researchers attempt to “scale up” their systems to handle more complicated, real world situations, the programs tend to become excessively brittle without commonsense knowledge or a rudimentary understanding of the situation. In other words, they fail as unexpected circumstances outside of its original problem context begin to appear. When human beings are dealing with new situations in the world, they know what to expect: they know what all things around them are, why they are there, what they are likely to do and so on. Humans can use context and experience to guide them in recognizing unusual situations and adjusting accordingly. A machine without strong AI has no other skills to fall back on so some machine decision-making applications are intractable.